The relationship between information technology (IT) and productivity
is widely discussed but little understood. Delivered computing-power
in the US economy has increased by more than two orders of magnitude
since 1970 (figure 1) yet productivity, especially in the service
sector, seems to have stagnated (figure 2). Given the enormous
promise of IT to usher in "the biggest technological revolution
men have known" (Snow, 1966), disillusionment and even frustration
with the technology is increasingly evident in statements like
"No, computers do not boost productivity, at least not most
of the time" (Economist, 1990).

The increased interest in the "productivity paradox,"
as it has become known, has engendered a significant amount of
research, but, thus far, this has only deepened the mystery. Robert
Solow, the Nobel Laureate economist, has aptly characterized the
results: "we see computers everywhere except in the productivity
statistics." Although similar conclusions are repeated by
an alarming number of researchers in this area, we must be careful
not to over interpret these findings; a shortfall of evidence
is not necessarily evidence of a shortfall. In fact, many of the
most widely cited aspects of the "paradox" do not stand
up to closer scrutiny.

This article summarizes what we know and don't know, distinguishes
the central issues from diversions, and clarifies the questions
that can be profitably explored in future research. After reviewing
and assessing the research to date, it appears that the shortfall
of IT productivity is as much due to deficiencies in our measurement
and methodological tool kit as to mismanagement by developers
and users of IT.

The research considered in this review reflects the results of
a computerized literature search of 30 of the leading journals
in both information systems and economics[1], as well as discussions
with leading researchers in the field. In what follows, I have
highlighted the key findings and essential research references.

Productivity is the fundamental economic measure of a technology's
contribution. With this in mind, CEOs and line managers have increasingly
begun to question their huge investments in computers and related
technologies. While major success stories exist, so do equally
impressive failures (see, for example (Kemerer & Sosa,
1990)). The lack of good quantitative measures for the output
and value created by IT has made the MIS manager's job of justifying
investments particularly difficult. Academics have had similar
problems assessing the contributions of this critical new technology,
and this has been generally interpreted as a negative signal of
its value.

The disappointment in IT has been chronicled in articles disclosing
broad negative correlations with economy-wide productivity and
information worker productivity. Econometric estimates have also
indicated low IT capital productivity in a variety of manufacturing
and service industries. The principal empirical research studies
of IT and productivity are listed in table 1.

One of the core issues for economists in the past decade has been
the productivity slowdown that began in the early 1970s. Even
after accounting for factors such as the oil price shocks, most
researchers find that there is an unexplained residual drop in
productivity as compared with the first half of the post-war period.
The sharp drop in productivity roughly coincided with the rapid
increase in the use of IT (figure 1). Although recent productivity
growth has rebounded somewhat, especially in manufacturing, the
overall negative correlation between economy-wide productivity
and the advent of computers is behind many of the arguments that
IT has not helped US productivity or even that IT investments
have been counter-productive.

This link is made more directly in research by Roach (1991) focusing
specifically on information workers, regardless of industry. While
in the past, office work was not very capital intensive, recently
the level of IT capital per ("white collar") information
worker has begun approaching that of production capital per ("blue
collar") production worker. Concurrently, the ranks of information
workers have ballooned and the ranks of production workers have
shrunk. Roach cites statistics indicating that output per production
worker grew by 16.9% between the mid-1970s and 1986, while output
per information worker decreased by 6.6%. He concludes: "We
have in essence isolated America's productivity shortfall and
shown it to be concentrated in that portion of the economy that
is the largest employer of white-collar workers and the most heavily
endowed with high-tech capital." Roach's analysis provides
quantitative support for widespread reports of low office productivity.[2]

Comment

Upon closer examination, the alarming correlation between higher
IT spending and lower productivity at the level of the entire
US economy is not compelling because so many other factors affect
productivity. Until recently, computers were not a major share
of the economy. Consider the following order-of-magnitude estimates.
Information technology capital stock is currently equal to about
10% of GNP. If, hypothetically, the return on IT investment were
20%, then current GNP would be directly increased about 2% (10%
x 20%) because of the existence of our current stock of IT. The
2% increase must be spread over about 30 years since that is how
long it took us to reach the current level of IT stock. This works
out to an average contribution to aggregate GNP growth of 0.06%
in each year. Although this amounts to billions of dollars, it
is very difficult to isolate in our five trillion dollar economy
because so many other factors affect GNP. Indeed, if the marginal
product of IT capital were anywhere from -20% to +40%, it would
still not have affected aggregate GNP growth by more than about
0.1% per year.[3]

This is not to say that computers may not have had significant
effects in specific areas, such as transaction processing, or
on other characteristics of the economy, such as employment shares,
organizational structure or product variety. Rather it suggests
that very large changes in capital stock are needed to measurably
affect total output under conventional assumptions about typical
rates of return. However, the growth in IT stock continues to
be significant. At current growth rates, we should begin to notice
changes at the level of aggregate GNP in the near future if computers
are productive.

As for the apparent stagnation in white collar productivity, one
should bear in mind that relative productivity cannot be directly
inferred from the number of information workers per unit output.
For instance, if a new delivery schedule optimizer allows a firm
to substitute a clerk for two truckers, the increase in the number
of white collar workers is evidence of an increase, not a decrease,
in their relative productivity and in the firm's productivity
as well. Osterman (1986) suggests that this is why clerical employment
often increases after the introduction of computers and Berndt
and Morrison (1991) confirm that IT capital is, on average, a
complement for white collar labor even as it leads to fewer blue
collar workers. Unfortunately, more direct measures of office
worker productivity are exceedingly difficult. Because of the
lack of hard evidence, Panko (1991) has gone so far as to call
the idea of stagnant office worker productivity a myth, although
he cites no evidence to the contrary.

A more direct case for weakness in IT's contribution comes from
the explicit evaluation of IT capital productivity, typically
by estimating the coefficients of a production function. This
has been done in both manufacturing and service industries, as
reviewed below.

There have been at least five studies of IT productivity in the
manufacturing sector, summarized in table 2.

A study by Loveman (1988) provided some of the first econometric
evidence of a potential problem when he examined data from 60
business units. As is common in the productivity literature, he
used ordinary least squares regression to estimate the parameters
of a production function. Loveman estimated that the contribution
of IT capital to output was approximately zero over the five year
period studied in almost every subsample he examined. His findings
were fairly robust to a number of variations on his basic formulation
and underscore the paradox: while firms were demonstrating a voracious
appetite for a rapidly-improving technology, measured productivity
gains were insignificant.

Barua, Kriebel and Mukhopadhyay (1991) traced the causal chain
back a step by looking at IT's effect on intermediate variables
such as capacity utilization, inventory turnover, quality, relative
price and new product introduction. Using the same data set, they
found that IT was positively related to three of these five intermediate
measures of performance, although the magnitude of the effect
was generally too small to measurably affect return on assets
or market share.

Using a different data set, Weill (1990) was also able to disaggregate
IT by use, and found that significant productivity could be attributed
to transactional types of IT (e.g. data processing), but was unable
to identify gains associated with strategic systems (e.g. sales
support) or informational investments (e.g. email infrastructure).

Morrison and Berndt have written a paper using a broader data
set from the US Bureau of Economic Analysis (BEA) that encompasses
the whole U.S. manufacturing sector (Morrison & Berndt,
1990). It examined a series of highly parameterized models of
production, found evidence that every dollar spent on IT delivered,
on average, only about $0.80 of value on the margin, indicating
a general overinvestment in IT.

Finally, Siegel and Griliches (1991) used industry and establishment
data from a variety of sources to examine several possible biases
in conventional productivity estimates. Among their findings was
a positive simple correlation between an industry's level of investment
in computers and its multifactor productivity growth in the 1980s.
They did not examine more structural approaches, in part because
of troubling concerns they raised regarding the reliability of
the data and government measurement techniques.

Table 2: Studies of IT in Manufacturing

Study

Data Source

Findings

(Loveman, 1988)

PIMS/MPIT

IT investments added nothing to output

(Weill, 1990)

Interviews and Surveys

Contextual variables affect IT performance

(Morrison & Berndt, 1990)

BEA

IT marginal benefit is 80 cents per dollar invested

(Barua, Kriebel & Mukhopadhyay, 1991)

PIMS/MPIT

IT improved intermediate outputs, if not necessarily final output

(Siegel & Griliches, 1991)

Multiple gov't sources

IT using industries tend to be more productive; government data is unreliable

Comment

All authors make a point of emphasizing the limitations of their
respective data sets. The MPIT data, which both Loveman and Barua,
Kriebel and Mukhopadhyay use, can be particularly unreliable.
As the authors are careful to point out, the results are based
on dollar denominated outputs and inputs, and therefore depend
on price indices which may not accurately account for changes
in quality or the competitive structure of the industry.

The BEA data may be somewhat more dependable but one of Siegel
and Griliches' principal conclusions was that "after auditing
the industry numbers, we found that a non-negligible number of
sectors were not consistently defined over time." However,
the generally reasonable estimates derived for the other, non-IT
factors of production in each of the studies indicate that there
may indeed be something worrisome, or at least special, about
IT.

It has been widely reported that most of the productivity slowdown
is concentrated in the service sector (Roach, 1991). Before about
1970, service productivity growth was comparable to that in manufacturing,
but since then the trends have diverged significantly. Meanwhile
services have dramatically increased as a share of total employment
and to a lesser extent, as a share of total output. Because services
use over 80% IT, this has been taken as indirect evidence of poor
IT productivity. The studies that have tried to assess IT productivity
in the service sector are summarized in table 3.

One of the first studies of IT's impact was by Cron and Sobol
(1983), who looked at a sample of wholesalers. They found that
on average, IT's impact was not significant, but that it seemed
to be associated with both very high and very low performers.
This finding has engendered the hypothesis that IT tends to reinforce
existing management approaches, helping well-organized firms succeed
but only further confusing managers who haven't properly structured
production in the first place.

Paul Strassmann also reports disappointing evidence in several
studies. In particular, he found that there was no correlation
between IT and return on investment in a sample of 38 service
sector firms: some top performers invest heavily in IT, while
some do not. In many of his studies, he used the same MPIT data
set discussed above and had similar results. He concludes that
"there is no relation between spending for computers, profits
and productivity" (Strassmann, 1990).

Roach's widely cited research on white collar productivity, discussed
above, focused principally on IT's dismal performance in the service
sector (1991; 1989a). Roach argues that IT is an effectively used
substitute for labor in most manufacturing industries, but has
paradoxically been associated with bloating white-collar employment
in services, especially finance. He attributes this to relatively
keener competitive pressures in manufacturing and foresees a period
of belt-tightening and restructuring in services as they also
become subject to international competition.

There have been several studies of IT's impact on the performance
of various types of financial services firms. A recent study by
Parsons, Gottlieb and Denny (1990) estimated a production function
for banking services in Canada and found that overall, the impact
of IT on multifactor productivity was quite low between 1974 and
1987. They speculate that IT has positioned the industry for greater
growth in the future. Similar conclusions are reached by Franke
(1987), who found that IT was associated with a sharp drop in
capital productivity and stagnation in labor productivity, but
remained optimistic about the future potential of IT, citing the
long time lags associated with previous "technological transformations"
such as the conversion to steam power.

Harris and Katz (1989) looked at data on the insurance industry
from the Life Office Management Association Information Processing
Database. They found a positive relationship between IT expense
ratios and various performance ratios although at times the relationship
was quite weak.

Alpar and Kim (1990) note that the methodology used to assess
IT impacts can also significantly affect the results. They applied
two approaches to the same data set. One approach was based on
key ratios and the other used a cost function derived from microeconomic
theory. They concluded that key ratios could be particularly misleading.

Table 3: Studies of IT in Services

Study

Data Source

Findings

(Cron & Sobol, 1983)

138 medical supply wholesalers

Bimodal distribution among high IT investors: either very good or very bad

(Strassmann, 1990)

Computerworld survey of 38 companies

No correlation between various IT ratios and performance measures

(Roach, 1991; Roach, 1989a)

Principally BLS, BEA

Vast increase in IT capital per information worker while measured output decreased

(Harris & Katz, 1989)

LOMA insurance data for 40

Weak positive relationship between IT and various performance ratios

(Noyelle, 1990)

US and French industry

Severe measurement Problems in services

(Alpar & Kim, 1990)

Federal Reserve Data

Performance estimates sensitive to methodology

(Parsons, Gotlieb & Denny, 1990)

Internal operating data from 2 large banks

IT coefficient in translog production function small and often negative

Comment

Measurement problems are even more acute in services than in manufacturing.
In part, this arises because many service transactions are idiosyncratic,
and therefore not subject to statistical aggregation. Unfortunately,
even when abundant data exist, classifications sometimes seem
arbitrary. For instance, in accordance with a fairly standard
approach, Parsons, Gottlieb and Denny (1990) treated time deposits
as inputs into the banking production function and demand deposits
as outputs. The logic for such decisions is often difficult to
fathom and subtle changes in deposit patterns or classification
standards can have disproportionate impacts.

The importance of variables other than IT also becomes particularly
apparent in some of the service sector studies. Cron and Sobol's
finding of a bimodal distribution suggests that some variable
was left out of the equation. Furthermore, researchers and consultants
have increasingly emphasized the theme of re-engineering work
when introducing major IT investments (Hammer, 1990). A frequently
cited example is the success of the Batterymarch services firm.
Batterymarch used IT to radically restructure the investment management
process, rather than simply overlaying IT on existing processes.

In sum, while a number of the dimensions of the "IT productivity
paradox" have been overstated, the question remains as to
whether IT is having the positive impact expected. In particular,
better measures of information worker productivity are needed,
as are explanations for why IT capital hasn't clearly improved
firm-level productivity in manufacturing and services. We now
examine four basic approaches taken to answer these questions.

Although it is too early to conclude that IT's productivity contribution
has been subpar, a paradox remains in our inability to unequivocally
document any contribution after so much effort. The various explanations
that have been proposed can be grouped into four categories:

1) Mismeasurement of outputs and inputs,

2)Lags due to learning and adjustment,

3)Redistribution and dissipation of profits,

4)Mismanagement of information and technology.

The first two explanations point to shortcomings in research,
not practice, as the root of the productivity paradox. It is possible
that the benefits of IT investment are quite large, but that a
proper index of its true impact has yet to be analyzed. Traditional
measures of the relationship between inputs and outputs fail
to account for non-traditional sources of value. Second,
if significant lags between cost and benefit may exist, then short-term
results look poor but ultimately the pay-off will be proportionately
larger. This would be the case if extensive learning, by both
individuals and organizations, were needed to fully exploit IT,
as it is for most radically new technologies.

A more pessimistic view is embodied in the other two explanations.
They propose that there really are no major benefits, now or in
the future, and seek to explain why managers would systematically
continue to invest in IT. The redistribution argument suggests
that those investing in the technology benefit privately but at
the expense of others, so no net benefits show up at the aggregate
level. The final type of explanation examined is that we have
systematically mismanaged IT: there is something in its nature
that leads firms or industries to invest in it when they shouldn't,
to misallocate it, or to use it to create slack instead of productivity.
Each of these four sets of hypotheses is assessed in turn below.

The easiest explanation for the low measured productivity of IT
is simply that we're not properly measuring output. Denison (1989)
makes a wide-ranging case that productivity and output statistics
can be very unreliable. Most economists would agree with the evidence
presented by Gordon and Baily (1989), and Noyelle (1990) that
the problems are particularly bad in service industries, which
happen to own the majority of IT capital. It is important to note
that measurement errors need not necessarily bias IT productivity
if they exist in comparable magnitudes both before and after IT
investments. However, the sorts of benefits ascribed by managers
to IT -- increased quality, variety, customer service, speed
and responsiveness -- are precisely the aspects of output
measurement that are poorly accounted for in productivity statistics
as well as in most firms' accounting numbers. This can lead to
systematic underestimates of IT productivity.

The measurement problems are particularly acute for IT use in
the service sector and among white collar workers. Since the null
hypothesis that no improvement occurred wins by default when no
measured improvement is found, it probably is not coincidental
that service sector and information worker productivity is considered
more of a problem than manufacturing and blue collar productivity,
where measures are better.

a. Output Mismeasurement

When comparing two output levels, it is important to deflate the
prices so they are in comparable "real" dollars. Accurate
price adjustment should remove not only the effects of inflation
but also adjust for any quality changes. Much of the measurement
problem arises from the difficulty of developing accurate, quality-adjusted
price deflators. Additional problems arise when new products or
features are introduced. This is not only because they have no
predecessors for direct comparison, but also because variety itself
has value, and that can be nearly impossible to measure.

The positive impact of IT on variety and the negative impact of
variety on measured productivity has been econometrically and
theoretically supported by Brooke (1991). He argues that lower
costs of information processing have enabled companies to handle
more products and more variations of existing products. However,
the increased scope has been purchased at the cost of reduced
economies of scale and has therefore resulted in higher unit costs
of output. For example, if a clothing manufacturer chooses to
produce more colors and sizes of shirts, which may have value
to consumers, existing productivity measures rarely account for
such value and will typically show higher "productivity"
in a firm that produces a single color and size. Higher prices
in industries with increasing product diversity is likely to be
attributed to inflation, despite the real increase in value provided
to consumers.

In services, the problem of unmeasured improvements can be even
worse than in manufacturing. For instance, the convenience afforded
by twenty-four hour ATMs is frequently cited as an unmeasured
quality improvement. How much value has this contributed to banking
customers? Government statistics implicitly assume it is all captured
in the number of transactions, or worse, that output is a constant
multiple of labor input!

In a case study of the finance, insurance and real estate sector,
where computer usage and the numbers of information workers are
particularly high, Baily and Gordon (1988) identified a number
of practices by the Bureau of Economic Analysis (BEA) which tend
to understate productivity growth. Their revisions add 2.3% per
year to productivity between 1973 and 1987 in this sector.

b. Input Mismeasurement

If the quality of work life is improved by computer usage (less
repetitive retyping, tedious tabulation and messy mimeos), then
theory suggests that proportionately lower wages can be paid.
Thus the slow growth in clerical wages may be compensated for
by unmeasured improvements in work life that are not accounted
for in government statistics.

A related measurement issue is how to measure IT stock itself.
For any given amount of output, if the level of IT stock used
is overestimated, then its unit productivity will appear to be
less than it really is. Denison (1989) argues the government overstates
the decline in the computer price deflator. If this is true, the
"real" quantity of computers purchased recently is not
as great as statistics show, while the "real" quantity
purchased 20 years ago is higher. The net result is that
much of the productivity improvement that the government attributes
to the computer-producing industry, should be allocated to computer-using
industries. Effectively, computer users have been "overcharged"
for their recent computer investments in the government productivity
calculations.

To the extent that complementary inputs, such as software, or
training, are required to make investments in IT worthwhile, labor
input may also be overestimated. Although spending on software
and training yields benefits for several years, it is generally
expensed in the same year that computers are purchased, artificially
raising the short-term costs associated with computerization.
In an era of annually rising investments, the subsequent benefits
would be masked by the subsequent expensing of the next, larger,
round of complementary inputs. On the other hand, IT purchases
may also create long-term liabilities in software and hardware
maintenance that are not fully accounted for, leading to an underestimate
of IT's impact on costs.

Comments

The closer one examines the data behind the studies of IT performance,
the more it looks like mismeasurement is at the core of the "productivity
paradox". Rapid innovation has made IT intensive industries
particularly susceptible to the problems associated with measuring
quality changes and valuing new products. The way productivity
statistics are currently kept can lead to bizarre anomalies: to
the extent that ATMs lead to fewer checks being written, they
can actually lower productivity statistics. Increased variety,
improved timeliness of delivery and personalized customer service
are additional benefits that are poorly represented in productivity
statistics. These are all qualities that are particularly likely
to be enhanced by IT. Because information is intangible, increases
in the implicit information content of products and services are
likely to be under-reported compared to increases in materials
content.

Nonetheless, some analysts remain skeptical that measurement problems
can explain much of the slowdown. They point out that by many
measures, service quality has gone down, not up. Furthermore,
they question the value of variety when it takes the form of six
dozen brands of breakfast cereal.

A second explanation for the paradox is that the benefits from
IT can take several years to show up on the bottom line.

The idea that new technologies may not have an immediate impact
is a common one in business. For instance, a survey of executives
suggested that many expected it to take at much as five years
for IT investments to pay-off. This accords with a recent econometric
study by Brynjolfsson et al. (l991a) which found lags of two to
three years before the strongest organizational impacts of IT
were felt. In general, while the benefits from investment in infrastructure
can be large, they are indirect and often not immediate.

The existence of lags has some basis in theory. Because of its
unusual complexity and novelty, firms and individual users of
IT may require some experience before becoming proficient. According
to models of learning-by-using, the optimal investment strategy
sets short term marginal costs greater than short-term marginal
benefits. This allows the firm to "ride" the learning
curve and reap benefits analogous to economies of scale. If only
short-term costs and benefits are measured, then it might appear
that the investment was inefficient.

Comment

If managers are rationally accounting for lags, this explanation
for low IT productivity growth is particularly optimistic. In
the future, not only should we reap the then-current benefits
of the technology, but also enough additional benefits to make
up for the extra costs we are currently incurring.

A third possible explanation is that IT may be beneficial to individual
firms, but unproductive from the standpoint of the industry as
a whole or the economy as a whole: IT rearranges the shares of
the pie without making it any bigger.

There are several arguments for why redistribution may be more
of a factor with IT investments than for other investments. For
instance, IT may be used disproportionately for market research
and marketing, activities which can be very beneficial to the
firm while adding nothing to total output (Baily & Chakrabarti,
1988). Furthermore, economists have recognized for some time that,
compared to other goods, information is particularly vulnerable
to rent dissipation, in which one firm's gain comes entirely at
the expense of others, instead of by creating new wealth. Advance
knowledge of demand, supply, weather or other conditions that
affect asset prices can be very profitable privately even without
increasing total output. This will lead to excessive incentives
for information gathering.

Comment

Unlike the other possible explanations, the redistribution hypothesis
would not explain any shortfall in IT productivity at the firm-level:
firms with inadequate IT budgets would lose market share and profits
to high IT spenders. In this way, an analogy could be made to
models of the costs and benefits of advertising. The recent popularity
of "strategic information systems" designed to take
profits from competitors rather than to lowers costs may be illustrative
of this thinking. On the other hand, the original impetus for
much of the spending on EDP was administrative cost reduction.
This is still the principal justification used in many firms.

A fourth possibility is that, on the whole, IT really is not productive
at the firm level. The investments are made nevertheless because
the decision-makers aren't acting in the interests of the firm.
Instead, they are increasing their slack, building inefficient
systems, or simply using outdated criteria for decision-making.

Many of the difficulties that researchers have in quantifying
the benefits of IT would also affect managers. As a result, they
may have difficulty in bringing the benefits to the bottom line
if output targets, work organization and incentives are not appropriately
adjusted. The result is that IT might increase organizational
slack instead of output or profits. This is consistent with arguments
by Roach (1989a) that manufacturing has made better use of IT
than has the service sector because manufacturing faces greater
international competition, and thus tolerates less slack.

Sometimes the benefits do not even appear in the most direct measures
of IT effectiveness. This stems not only from the intrinsic difficulty
of system design and software engineering, but also because the
rapidly-evolving technology leaves little time for time-tested
principles to diffuse before being supplanted.

A related argument derives from evolutionary models of organizations.
The difficulties in measuring the benefits of information and
IT discussed above may also lead to the use of heuristics, rather
than strict cost/benefit accounting to set levels of IT investments.[4]
Our current institutions, heuristics and management principles
evolved largely in a world with little IT. The radical changes
enabled by IT may make these institutions outdated. For instance,
a valuable heuristic in 1960 might have been "get all readily
available information before making a decision." The same
heuristic today could lead to information overload and chaos (Thurow,
1987). Indeed, the rapid speed-up enabled by IT can create unanticipated
bottlenecks at each human in the information processing chain.
More money spent on IT won't help until these bottlenecks are
addressed. Successful IT implementation process must not simply
overlay new technology on old processes.

At a broader level, several researchers suggest that our currently
low productivity levels are symptomatic of an economy in transition,
in this case to the "information era" (David, 1989;
Franke, 1987). For instance, David makes an analogy to the electrification
of factories at the turn of the century. Major productivity gains
did not occur for twenty years, when new factories were designed
and built to take advantage of electricity's flexibility which
enabled machines to be located based on work-flow efficiency,
instead of proximity to waterwheels, steam-engines and powertransmitting
shafts and rods.

Comments

While the idea of firms consistently making inefficient investments
in IT is anathema to the neoclassical view of the firm as a profit-maximizer,
it can be explained formally by models such as agency theory and
evolutionary economics, which treat the firm as a more complex
entity. The fact that firms continue to invest large sums in the
technology suggests that the individuals within the firm that
make investment decisions are getting some benefit or at least
believe they are getting some benefit from IT.

In general, however, we do not yet have comprehensive models of
the internal organization of the firm and researchers, at least
in economics, are mostly silent on the sorts of inefficiency discussed
in this section.

Research on IT and productivity has been disappointing, not only
because it has only exacerbated apprehension about the ultimate
value of billions of dollars of IT investment, but also because
it has raised frustrating concerns with the measures and methods
commonly used for productivity assessment. However, only by understanding
the causes of the "productivity paradox", we can learn
how to identify and remove the obstacles to higher productivity
growth.

Section II presented a review of the principal empirical
literature that engendered the term "productivity paradox"
regarding poor IT performance. While a number of dimensions of
the paradox are disturbing and provoking, we still do not have
a definitive answer to the question of whether IT's productivity
impact actually has been unusually low.

Section III focused on identifying explanations for a slightly
redefined "paradox": Why have we been unable to document
any productivity gains from IT thus far? The four principal hypotheses
summarized in the adjoining sidebar.

It is common to focus only on the mismanagement explanation, but
a closer examination of the principal studies and the underlying
data underscores the possibility that measurement difficulties
may account for the lion's share of the gap between our expectations
for the technology and its apparent performance.

Even with substantive improvements in our research on IT and productivity,
researchers must not overlook that fact that our tools are still
blunt. Managers do not always recognize this and tend to give
a great deal of weight to studies of IT and productivity. Because
they are written for an academic audience, the studies themselves
are usually careful to spell out the limitations of the data and
methods, but sometimes only the surprising conclusions are reported
by the media. Because significant investment decisions are based
on these conditions, researchers must be doubly careful to communicate
the limitations as well.

Beyond Productivity and Productivity Measurement

While the focus of this paper has been on the productivity literature,
in business-oriented journals a recurrent theme is the ideas that
IT will not so much help us produce more of the same things as
allow us to do entirely new things in new ways (Hammer, 1990;
Malone & Rockart, 1991). For instance, Brooke (1991)
makes a connection to greater variety but lower productivity as
traditionally measured. The business transformation literature
highlights how difficult and perhaps inappropriate it would be
to try to translate the benefits of IT usage into quantifiable
productivity measures of output. Intangibles such as better responsiveness
to customers and increased coordination with suppliers do not
always increase the amount or even intrinsic quality of output,
but they do help make sure it arrives at the right time, at the
right place, with the right attributes for each customer. Just
as managers look beyond "productivity" for some of the
benefits of IT, so must researchers be prepared to look beyond
conventional productivity measurement techniques.

If the value of IT remains unproved, the one certainty is that
the measurement problem is becoming more severe. Developed nations
are devoting increasing shares of their economies to service-
and information-intensive activities for which output measures
are poor. The comparison of the emerging "information age"
to the industrial revolution has prompted a new approach to management
accounting (Kaplan, 1989). A review of the IT productivity research
indicates an analogous opportunity to rethink the way we measure
productivity and output.

This research was sponsored by the MIT Center for Coordination
Science, the MIT International Financial Services Research Center,
and the MIT Industrial Performance Center. Special thanks are
due Michael Dertouzos and Tom Malone for inviting me to pursue
this topic for a study at the MIT Laboratory for Computer Science.
I would like to thank Ernie Berndt, Geoffrey Brooke, Chris Kemerer,
Richard Lester, Jack Rockart and seminar participants in Cambridge,
New York, and London for valuable comments. Marshall van Alstyne
provided excellent research assistance.

Cron, W.L. and Sobol, M.G. The Relationship Between Computerization
and Performance: A Strategy for Maximizing the Economic Benefits
of Computerization. Journal of Information and Management Vol.
6, (1983), pp. 171-181.

Harris, S.E. and Katz, J.L. Predicting Organizational Performance
Using Information Technology Managerial Control Ratios. In
Proceedings of the Twenty-Second Hawaii International Conference
on System Science (1989, Honolulu, HI).

Morrison, C.J. and Berndt, E.R. Assessing the Productivity
of Information Technology Equipment in the U.S. Manufacturing
Industries . National Bureau of Economic Research Working
Paper #3582, (January, 1990).

[1] The joumals searched included
Communications of ~he ACM, Database, Datamation, Decision Sciences, ~arvard
Business Review, IEEE Spectrum, IEEE Transactions on Engineering Management,
IEEE Transactions on Software Engineering, Information & Management,
Interfaces, Journal of Systems Management, Management Science, MIS Quarterly,
Operations Research, Sloan Management Review, American Economic Review, Bell
(Rand) Journal of Economics, Brookings Papers on Economics and Accounting,
Econometrica, Economic Development Review, Economica, Economics Journal,
Economist (Netherlands), Information Economics & Policy, International
Economics Review, and the Journal of Business Finance. Articles were
selected if they indicated an emphasis on computers, information systems,
information technology, decision support systems, expert systems, or high
technology combined with an emphasis on productivity. A longer version of this
paper, including comprehensive bibliography of articles in this area is
available from the author.

[2] For instance, Lester Thurow has noted that "the American factory
works, the American office doesn't", citing examples from the auto industry
indicating that Japanese managers are able to get more output from blue collar
workers (even in American plants) with up to 40% fewer managers.

[3] In dollar terms, each white collar worker is endowed with about
$10,000 in IT capital, which at a 20% ROI, would increase his or her total
output about by about $2000 per year as compared with pre-computer levels of
output. Compare to the $100,000 or so in salary and overhead that it costs to
employ this worker and the expectations for a technological "silver bullet"
seem rather ambitious.

[4] Indeed, a recent review of the techniques used by major companies
to justify IT investments revealed surprisingly little formal analysis. See
Clemons (1991) for an assessment of the IT justification process.

[5]
This comparison was inspired by the slightly exaggerated claim in Forbes,
(1980), that "If the auto industry had done what the computer industry has
done, ... a Rolls-Royce would cost $2.50 and get 2,000,000 miles to the
gallon."